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Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus

Metadata Updated: February 22, 2025

In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatio-temporally dependent and the mixing system may be non-stationary. Here, we propose a DCA algorithm, that combines concepts of particle filters and Markov Chain Monte Carlo (MCMC) methods in order to separate non-stationary mixtures of spatially dependent Gaussian sources.

Access & Use Information

Public: This dataset is intended for public access and use. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.

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Dates

Metadata Created Date February 22, 2025
Metadata Updated Date February 22, 2025
Data Update Frequency irregular

Metadata Source

Harvested from nasa test json

Additional Metadata

Resource Type Dataset
Metadata Created Date February 22, 2025
Metadata Updated Date February 22, 2025
Publisher Dashlink
Maintainer
Identifier DASHLINK_215
Data First Published 2010-09-22
Data Last Modified 2025-02-19
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id f9c3f6e3-e721-4daf-b1b2-cfea6d06e070
Harvest Source Id a73e0c30-4684-40ef-908e-d22e9e9e5f86
Harvest Source Title nasa test json
Homepage URL https://c3.nasa.gov/dashlink/resources/215/
Program Code 026:029
Source Datajson Identifier True
Source Hash efd2198e6d0a7cc31698860897f5ba1ed0c0e9f7533be45572201a91b93a44f4
Source Schema Version 1.1

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